Reliability in Psychological Measurement

Reliability in Psychological Measurement

• Reliability refers to the consistency of measurement. A reliable measure produces similar results when repeated. Physical science measurements often have clear reliability, but psychological phenomena are less stable, requiring measures designed for reliability.

• Reliability is critical in serious decision-making based on test results, e.g., educational, professional, or diagnostic decisions. Test developers aim to maximize test reliability and report it.

• Reliability is one quality of a good psychological test; the others are validity and standardization. A test's lack of reliability makes it untrustworthy.

• Reliability is the consistency of scores of individuals on the same test on different occasions, with various versions of the test, or under varying conditions. This relates to consistency in the measurement process and freedom from measurement errors.

• Reliability is linked to correlation and measurement error. According to theory, a perfect measure (free of error) would produce a true score. However, all measures have some error. Observed scores are the combination of true scores and these measurement errors.

Sources of Error in Test Scores

• Test-related factors can cause measurement inconsistencies:

• Difficulty level (excessively easy or hard)

• Length (excessively long, leading to fatigue)

• Content appropriateness (not all content can be suitable for all)

• Items may not fully reflect the intended domain

• Time constraints

• Test administration processes can introduce error:

• Poor testing conditions (environment, setting)

• Inconsistent instructions

• Test administrator bias (individual differences in personality/approach)

• Rapport (e.g., test taker connection with administrator)

• Examinee-related factors can also cause error:

• Prior knowledge and experience

• Individual differences (personality, emotional states, motivation, knowledge)

• Differences from the standardized sample (every group is unique!)

Correlation and Reliability

• Correlation coefficients (r) measure the relationship between two variables. They range from +1.0 to -1.0. A coefficient of +1 means perfect positive correlation, while -1 means perfect negative correlation. 0 signifies no correlation.

• The magnitude of the correlation coefficient indicates the strength of the relationship: closer to +1 or -1 is stronger, towards zero means weaker.

• Direction (sign) indicates the type of relationship: positive means variables increase/decrease together, negative means one increases as other decreases.

• Correlation illustrates the consistency of scores across different samples.

• Reliability estimates are often calculated to determine consistency.

• Scatter plots are useful for visualizing the correlation between two variables. A perfect positive correlation appears as a straight line from lower left to upper right, and for negative correlation the line is from upper left to lower right.

Types of Correlations (examples from the text):

• Positive Correlation (IQ and Math Scores): Higher IQ scores tend to be associated with higher math scores.
• Negative Correlation (IQ and Missed Classes): Higher IQ scores tend to be associated with lower numbers of missed classes
• Zero Correlation (Math and English Scores): No discernable relationship between math and English scores.

Note on Significance and Reliability Coefficients:

• Reliability coefficients (e.g., .80 or .90) are often used in practice, but even values below .80 may be acceptable with large samples.
• Significance tables exist to evaluate the statistical significance of correlation coefficients. A calculated coefficient may need to meet a certain criterion of statistical certainty of the results, which is usually around .80.